The use of nontraditional stabilizers for construction of airports in Alaska
Bibliographic record
Abstract
The cost of constructing unsurfaced gravel airports in rural Alaska can easily reach between $30 and $40 million due to the lack of gravel. The lower Kuskokwim and Yukon River delta soils consist entirely of fine silts and sands. Consequently, any gravel required must be imported at a cost of $300 to $600 per cubic yard ($392 to $785 per cubic meter). In an effort to reduce these costs, the Alaska University Transportation Center, in partnership with the Alaska Department of Transportation and Public Facilities developed the use of two part chemical stabilizers to stabilize local marginal materials. Several stabilizing products were incorporated in silt and sand in an effort to find the optimal and most cost effective stabilizer. These products included geofiber, chemical stabilizers, and curing agents. Laboratory work included California Bearing Ratio and unconfined compressive strength tests on Horseshoe Lake sand, Fairbanks silt, and other standard sands. Test results revealed that sand-geofiber mixtures should contain an optimum amount of fines (siIt) to mobilize the mix strength effectively. It was also found that the inclusion of an optimum geofiber content in Fairbanks silt (0.2% geofiber by weight) and in Horseshoe Lake sand (0.5% geofiber by weight) maximizes their hearing capacity. Using chemical stabilizers in conjunction with curing additives, hearing capacity was also enhanced as evidenced by unconfined compressive strengths of 1,100 psi (7.6 MPa) achieved for the sands and 600 psi (4.1 MPa) for the silts. This research showed that strength and hearing capacity enhancement of Alaskan marginal soils at airfield construction sites is feasible and cost-effective through the use of nontraditional stabilizers. Future research will evaluate the durability and freeze-thaw susceptibility of these optimized soil-stabilizer mixes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".